Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations60428
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 MiB
Average record size in memory320.0 B

Variable types

Categorical24
Numeric14
Boolean1
Text1

Alerts

SRP is highly overall correlated with store_cost(in millions) and 1 other fieldsHigh correlation
avg. yearly_income is highly overall correlated with education and 1 other fieldsHigh correlation
avg_cars_at home(approx) is highly overall correlated with avg_cars_at home(approx).1High correlation
avg_cars_at home(approx).1 is highly overall correlated with avg_cars_at home(approx)High correlation
coffee_bar is highly overall correlated with florist and 10 other fieldsHigh correlation
education is highly overall correlated with avg. yearly_incomeHigh correlation
florist is highly overall correlated with coffee_bar and 10 other fieldsHigh correlation
food_category is highly overall correlated with food_department and 1 other fieldsHigh correlation
food_department is highly overall correlated with food_category and 1 other fieldsHigh correlation
food_family is highly overall correlated with food_category and 1 other fieldsHigh correlation
frozen_sqft is highly overall correlated with coffee_bar and 10 other fieldsHigh correlation
grocery_sqft is highly overall correlated with coffee_bar and 9 other fieldsHigh correlation
gross_weight is highly overall correlated with net_weightHigh correlation
marital_status is highly overall correlated with num_children_at_homeHigh correlation
meat_sqft is highly overall correlated with coffee_bar and 10 other fieldsHigh correlation
member_card is highly overall correlated with avg. yearly_incomeHigh correlation
net_weight is highly overall correlated with gross_weightHigh correlation
num_children_at_home is highly overall correlated with marital_statusHigh correlation
prepared_food is highly overall correlated with coffee_bar and 10 other fieldsHigh correlation
salad_bar is highly overall correlated with coffee_bar and 10 other fieldsHigh correlation
sales_country is highly overall correlated with frozen_sqft and 5 other fieldsHigh correlation
store_city is highly overall correlated with coffee_bar and 11 other fieldsHigh correlation
store_cost(in millions) is highly overall correlated with SRP and 1 other fieldsHigh correlation
store_sales(in millions) is highly overall correlated with SRP and 1 other fieldsHigh correlation
store_sqft is highly overall correlated with coffee_bar and 11 other fieldsHigh correlation
store_state is highly overall correlated with coffee_bar and 11 other fieldsHigh correlation
store_type is highly overall correlated with coffee_bar and 10 other fieldsHigh correlation
video_store is highly overall correlated with coffee_bar and 10 other fieldsHigh correlation
total_children has 5624 (9.3%) zerosZeros
num_children_at_home has 37609 (62.2%) zerosZeros

Reproduction

Analysis started2024-09-15 07:43:30.719753
Analysis finished2024-09-15 07:44:31.844102
Duration1 minute and 1.12 second
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

food_category
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Vegetables
7440 
Snack Foods
6919 
Dairy
3835 
Meat
 
3107
Fruit
 
3080
Other values (40)
36047 

Length

Max length20
Median length16
Mean length10.466489
Min length4

Characters and Unicode

Total characters632469
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBreakfast Foods
2nd rowBreakfast Foods
3rd rowBreakfast Foods
4th rowBreakfast Foods
5th rowBreakfast Foods

Common Values

ValueCountFrequency (%)
Vegetables 7440
 
12.3%
Snack Foods 6919
 
11.4%
Dairy 3835
 
6.3%
Meat 3107
 
5.1%
Fruit 3080
 
5.1%
Jams and Jellies 2550
 
4.2%
Baking Goods 1947
 
3.2%
Breakfast Foods 1946
 
3.2%
Bread 1797
 
3.0%
Canned Soup 1722
 
2.8%
Other values (35) 26085
43.2%

Length

2024-09-15T13:14:32.009344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
foods 9968
 
10.3%
vegetables 7619
 
7.9%
snack 6919
 
7.2%
products 4667
 
4.8%
and 4140
 
4.3%
dairy 3835
 
4.0%
meat 3107
 
3.2%
fruit 3080
 
3.2%
canned 3071
 
3.2%
jams 2550
 
2.6%
Other values (49) 47394
49.2%

Most occurring characters

ValueCountFrequency (%)
e 75716
 
12.0%
a 59703
 
9.4%
s 47208
 
7.5%
o 38989
 
6.2%
35922
 
5.7%
r 33840
 
5.4%
n 33330
 
5.3%
t 32034
 
5.1%
d 30804
 
4.9%
i 26744
 
4.2%
Other values (32) 218179
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 504337
79.7%
Uppercase Letter 92210
 
14.6%
Space Separator 35922
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 75716
15.0%
a 59703
11.8%
s 47208
9.4%
o 38989
 
7.7%
r 33840
 
6.7%
n 33330
 
6.6%
t 32034
 
6.4%
d 30804
 
6.1%
i 26744
 
5.3%
l 21064
 
4.2%
Other values (13) 104905
20.8%
Uppercase Letter
ValueCountFrequency (%)
F 15089
16.4%
S 12995
14.1%
B 11265
12.2%
P 9401
10.2%
V 7619
8.3%
C 6932
7.5%
D 6859
7.4%
J 5860
 
6.4%
M 4272
 
4.6%
E 3091
 
3.4%
Other values (8) 8827
9.6%
Space Separator
ValueCountFrequency (%)
35922
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 596547
94.3%
Common 35922
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 75716
 
12.7%
a 59703
 
10.0%
s 47208
 
7.9%
o 38989
 
6.5%
r 33840
 
5.7%
n 33330
 
5.6%
t 32034
 
5.4%
d 30804
 
5.2%
i 26744
 
4.5%
l 21064
 
3.5%
Other values (31) 197115
33.0%
Common
ValueCountFrequency (%)
35922
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 632469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 75716
 
12.0%
a 59703
 
9.4%
s 47208
 
7.5%
o 38989
 
6.2%
35922
 
5.7%
r 33840
 
5.4%
n 33330
 
5.3%
t 32034
 
5.1%
d 30804
 
4.9%
i 26744
 
4.2%
Other values (32) 218179
34.5%

food_department
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Produce
8521 
Snack Foods
6919 
Household
6185 
Frozen Foods
6126 
Baking Goods
4497 
Other values (17)
28180 

Length

Max length19
Median length15
Mean length10.102535
Min length4

Characters and Unicode

Total characters610476
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrozen Foods
2nd rowFrozen Foods
3rd rowFrozen Foods
4th rowFrozen Foods
5th rowFrozen Foods

Common Values

ValueCountFrequency (%)
Produce 8521
14.1%
Snack Foods 6919
11.4%
Household 6185
10.2%
Frozen Foods 6126
10.1%
Baking Goods 4497
7.4%
Canned Foods 4238
7.0%
Dairy 3835
 
6.3%
Health and Hygiene 3807
 
6.3%
Beverages 3014
 
5.0%
Deli 2787
 
4.6%
Other values (12) 10499
17.4%

Length

2024-09-15T13:14:32.211149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
foods 19164
20.1%
produce 8521
 
8.9%
snack 6919
 
7.2%
goods 6294
 
6.6%
household 6185
 
6.5%
frozen 6126
 
6.4%
canned 4638
 
4.9%
beverages 4604
 
4.8%
baking 4497
 
4.7%
dairy 3835
 
4.0%
Other values (16) 24707
25.9%

Most occurring characters

ValueCountFrequency (%)
o 83844
13.7%
e 58406
 
9.6%
d 52152
 
8.5%
s 41111
 
6.7%
a 40057
 
6.6%
n 35970
 
5.9%
35062
 
5.7%
r 26563
 
4.4%
F 25290
 
4.1%
c 23017
 
3.8%
Other values (21) 189004
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 483731
79.2%
Uppercase Letter 91683
 
15.0%
Space Separator 35062
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 83844
17.3%
e 58406
12.1%
d 52152
10.8%
s 41111
8.5%
a 40057
8.3%
n 35970
7.4%
r 26563
 
5.5%
c 23017
 
4.8%
i 18458
 
3.8%
l 17155
 
3.5%
Other values (9) 86998
18.0%
Uppercase Letter
ValueCountFrequency (%)
F 25290
27.6%
H 13799
15.1%
B 11676
12.7%
S 9935
 
10.8%
P 9892
 
10.8%
D 6622
 
7.2%
G 6294
 
6.9%
C 5248
 
5.7%
A 1590
 
1.7%
E 952
 
1.0%
Space Separator
ValueCountFrequency (%)
35062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 575414
94.3%
Common 35062
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 83844
14.6%
e 58406
 
10.2%
d 52152
 
9.1%
s 41111
 
7.1%
a 40057
 
7.0%
n 35970
 
6.3%
r 26563
 
4.6%
F 25290
 
4.4%
c 23017
 
4.0%
i 18458
 
3.2%
Other values (20) 170546
29.6%
Common
ValueCountFrequency (%)
35062
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 610476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 83844
13.7%
e 58406
 
9.6%
d 52152
 
8.5%
s 41111
 
6.7%
a 40057
 
6.6%
n 35970
 
5.9%
35062
 
5.7%
r 26563
 
4.4%
F 25290
 
4.1%
c 23017
 
3.8%
Other values (21) 189004
31.0%

food_family
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Food
43284 
Non-Consumable
11573 
Drink
5571 

Length

Max length14
Median length4
Mean length6.0073641
Min length4

Characters and Unicode

Total characters363013
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFood
2nd rowFood
3rd rowFood
4th rowFood
5th rowFood

Common Values

ValueCountFrequency (%)
Food 43284
71.6%
Non-Consumable 11573
 
19.2%
Drink 5571
 
9.2%

Length

2024-09-15T13:14:32.418070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:32.584210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
food 43284
71.6%
non-consumable 11573
 
19.2%
drink 5571
 
9.2%

Most occurring characters

ValueCountFrequency (%)
o 109714
30.2%
F 43284
 
11.9%
d 43284
 
11.9%
n 28717
 
7.9%
e 11573
 
3.2%
l 11573
 
3.2%
b 11573
 
3.2%
a 11573
 
3.2%
m 11573
 
3.2%
u 11573
 
3.2%
Other values (8) 68576
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 279439
77.0%
Uppercase Letter 72001
 
19.8%
Dash Punctuation 11573
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 109714
39.3%
d 43284
 
15.5%
n 28717
 
10.3%
e 11573
 
4.1%
l 11573
 
4.1%
b 11573
 
4.1%
a 11573
 
4.1%
m 11573
 
4.1%
u 11573
 
4.1%
s 11573
 
4.1%
Other values (3) 16713
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
F 43284
60.1%
C 11573
 
16.1%
N 11573
 
16.1%
D 5571
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 11573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 351440
96.8%
Common 11573
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 109714
31.2%
F 43284
 
12.3%
d 43284
 
12.3%
n 28717
 
8.2%
e 11573
 
3.3%
l 11573
 
3.3%
b 11573
 
3.3%
a 11573
 
3.3%
m 11573
 
3.3%
u 11573
 
3.3%
Other values (7) 57003
16.2%
Common
ValueCountFrequency (%)
- 11573
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 109714
30.2%
F 43284
 
11.9%
d 43284
 
11.9%
n 28717
 
7.9%
e 11573
 
3.2%
l 11573
 
3.2%
b 11573
 
3.2%
a 11573
 
3.2%
m 11573
 
3.2%
u 11573
 
3.2%
Other values (8) 68576
18.9%

store_sales(in millions)
Real number (ℝ)

HIGH CORRELATION 

Distinct1033
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5410306
Minimum0.51
Maximum22.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:32.790816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile1.8
Q13.81
median5.94
Q38.67
95-th percentile13.08
Maximum22.92
Range22.41
Interquartile range (IQR)4.86

Descriptive statistics

Standard deviation3.4630465
Coefficient of variation (CV)0.52943439
Kurtosis0.093002343
Mean6.5410306
Median Absolute Deviation (MAD)2.38
Skewness0.67838277
Sum395261.4
Variance11.992691
MonotonicityNot monotonic
2024-09-15T13:14:33.016635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.04 311
 
0.5%
7.95 272
 
0.5%
5.4 270
 
0.4%
4.8 268
 
0.4%
7.41 265
 
0.4%
5.52 261
 
0.4%
6.84 257
 
0.4%
8.52 248
 
0.4%
3.6 244
 
0.4%
2.28 243
 
0.4%
Other values (1023) 57789
95.6%
ValueCountFrequency (%)
0.51 2
< 0.1%
0.52 3
< 0.1%
0.53 3
< 0.1%
0.54 1
 
< 0.1%
0.55 2
< 0.1%
0.56 1
 
< 0.1%
0.57 4
< 0.1%
0.58 3
< 0.1%
0.6 3
< 0.1%
0.61 2
< 0.1%
ValueCountFrequency (%)
22.92 1
 
< 0.1%
19.9 5
 
< 0.1%
19.85 3
 
< 0.1%
19.8 3
 
< 0.1%
19.75 19
< 0.1%
19.7 5
 
< 0.1%
19.65 2
 
< 0.1%
19.6 3
 
< 0.1%
19.55 5
 
< 0.1%
19.5 3
 
< 0.1%

store_cost(in millions)
Real number (ℝ)

HIGH CORRELATION 

Distinct9919
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6194595
Minimum0.1632
Maximum9.7265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:33.232848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.1632
5-th percentile0.70937
Q11.5
median2.3856
Q33.484025
95-th percentile5.34452
Maximum9.7265
Range9.5633
Interquartile range (IQR)1.984025

Descriptive statistics

Standard deviation1.4530087
Coefficient of variation (CV)0.55469791
Kurtosis0.54223178
Mean2.6194595
Median Absolute Deviation (MAD)0.97585
Skewness0.8329205
Sum158288.7
Variance2.1112343
MonotonicityNot monotonic
2024-09-15T13:14:33.479871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.512 71
 
0.1%
2.16 65
 
0.1%
3.024 64
 
0.1%
2.592 62
 
0.1%
1.728 62
 
0.1%
2.016 61
 
0.1%
1.584 58
 
0.1%
2.052 58
 
0.1%
2.352 57
 
0.1%
1.368 57
 
0.1%
Other values (9909) 59813
99.0%
ValueCountFrequency (%)
0.1632 1
< 0.1%
0.1705 1
< 0.1%
0.176 1
< 0.1%
0.1792 1
< 0.1%
0.186 1
< 0.1%
0.1953 2
< 0.1%
0.2013 1
< 0.1%
0.2014 1
< 0.1%
0.2028 1
< 0.1%
0.2052 2
< 0.1%
ValueCountFrequency (%)
9.7265 1
< 0.1%
9.5305 1
< 0.1%
9.525 1
< 0.1%
9.504 1
< 0.1%
9.408 1
< 0.1%
9.384 1
< 0.1%
9.3345 1
< 0.1%
9.2825 2
< 0.1%
9.2 1
< 0.1%
9.1885 1
< 0.1%

unit_sales(in millions)
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0931687
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:33.669961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82767691
Coefficient of variation (CV)0.26758221
Kurtosis-0.31897095
Mean3.0931687
Median Absolute Deviation (MAD)1
Skewness0.052504175
Sum186914
Variance0.68504907
MonotonicityNot monotonic
2024-09-15T13:14:33.858854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 27482
45.5%
4 16581
27.4%
2 13417
22.2%
5 2058
 
3.4%
1 864
 
1.4%
6 26
 
< 0.1%
ValueCountFrequency (%)
1 864
 
1.4%
2 13417
22.2%
3 27482
45.5%
4 16581
27.4%
5 2058
 
3.4%
6 26
 
< 0.1%
ValueCountFrequency (%)
6 26
 
< 0.1%
5 2058
 
3.4%
4 16581
27.4%
3 27482
45.5%
2 13417
22.2%
1 864
 
1.4%

promotion_name
Categorical

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Weekend Markdown
 
2330
Two Day Sale
 
2321
Price Savers
 
2279
Price Winners
 
2108
Save-It Sale
 
2001
Other values (44)
49389 

Length

Max length23
Median length21
Mean length14.170848
Min length9

Characters and Unicode

Total characters856316
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBag Stuffers
2nd rowCash Register Lottery
3rd rowHigh Roller Savings
4th rowCash Register Lottery
5th rowDouble Down Sale

Common Values

ValueCountFrequency (%)
Weekend Markdown 2330
 
3.9%
Two Day Sale 2321
 
3.8%
Price Savers 2279
 
3.8%
Price Winners 2108
 
3.5%
Save-It Sale 2001
 
3.3%
Super Duper Savers 1986
 
3.3%
Super Savers 1930
 
3.2%
One Day Sale 1843
 
3.0%
Double Down Sale 1755
 
2.9%
High Roller Savings 1741
 
2.9%
Other values (39) 40134
66.4%

Length

2024-09-15T13:14:34.055529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sale 10313
 
6.8%
price 10173
 
6.7%
savers 9898
 
6.5%
days 7459
 
4.9%
savings 6401
 
4.2%
for 5677
 
3.7%
one 4378
 
2.9%
super 4305
 
2.8%
day 4164
 
2.7%
two 3814
 
2.5%
Other values (58) 85647
56.3%

Most occurring characters

ValueCountFrequency (%)
e 105202
 
12.3%
91801
 
10.7%
r 65194
 
7.6%
a 64785
 
7.6%
s 49554
 
5.8%
S 44145
 
5.2%
i 40668
 
4.7%
l 38186
 
4.5%
o 35283
 
4.1%
n 34382
 
4.0%
Other values (33) 287116
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 610819
71.3%
Uppercase Letter 151695
 
17.7%
Space Separator 91801
 
10.7%
Dash Punctuation 2001
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 105202
17.2%
r 65194
10.7%
a 64785
10.6%
s 49554
 
8.1%
i 40668
 
6.7%
l 38186
 
6.3%
o 35283
 
5.8%
n 34382
 
5.6%
t 26454
 
4.3%
v 21700
 
3.6%
Other values (12) 129411
21.2%
Uppercase Letter
ValueCountFrequency (%)
S 44145
29.1%
D 23433
15.4%
P 12221
 
8.1%
B 8925
 
5.9%
T 8805
 
5.8%
C 7044
 
4.6%
W 6345
 
4.2%
G 5929
 
3.9%
O 5528
 
3.6%
I 5466
 
3.6%
Other values (9) 23854
15.7%
Space Separator
ValueCountFrequency (%)
91801
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 762514
89.0%
Common 93802
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 105202
13.8%
r 65194
 
8.5%
a 64785
 
8.5%
s 49554
 
6.5%
S 44145
 
5.8%
i 40668
 
5.3%
l 38186
 
5.0%
o 35283
 
4.6%
n 34382
 
4.5%
t 26454
 
3.5%
Other values (31) 258661
33.9%
Common
ValueCountFrequency (%)
91801
97.9%
- 2001
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 856316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 105202
 
12.3%
91801
 
10.7%
r 65194
 
7.6%
a 64785
 
7.6%
s 49554
 
5.8%
S 44145
 
5.2%
i 40668
 
4.7%
l 38186
 
4.5%
o 35283
 
4.1%
n 34382
 
4.0%
Other values (33) 287116
33.5%

sales_country
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
USA
38892 
Mexico
17572 
Canada
3964 

Length

Max length6
Median length3
Mean length4.0691732
Min length3

Characters and Unicode

Total characters245892
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA 38892
64.4%
Mexico 17572
29.1%
Canada 3964
 
6.6%

Length

2024-09-15T13:14:34.219639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:34.348641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
usa 38892
64.4%
mexico 17572
29.1%
canada 3964
 
6.6%

Most occurring characters

ValueCountFrequency (%)
U 38892
15.8%
S 38892
15.8%
A 38892
15.8%
M 17572
7.1%
e 17572
7.1%
x 17572
7.1%
i 17572
7.1%
c 17572
7.1%
o 17572
7.1%
a 11892
 
4.8%
Other values (3) 11892
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 138212
56.2%
Lowercase Letter 107680
43.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17572
16.3%
x 17572
16.3%
i 17572
16.3%
c 17572
16.3%
o 17572
16.3%
a 11892
11.0%
n 3964
 
3.7%
d 3964
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
U 38892
28.1%
S 38892
28.1%
A 38892
28.1%
M 17572
12.7%
C 3964
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 245892
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 38892
15.8%
S 38892
15.8%
A 38892
15.8%
M 17572
7.1%
e 17572
7.1%
x 17572
7.1%
i 17572
7.1%
c 17572
7.1%
o 17572
7.1%
a 11892
 
4.8%
Other values (3) 11892
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 38892
15.8%
S 38892
15.8%
A 38892
15.8%
M 17572
7.1%
e 17572
7.1%
x 17572
7.1%
i 17572
7.1%
c 17572
7.1%
o 17572
7.1%
a 11892
 
4.8%
Other values (3) 11892
 
4.8%

marital_status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
S
30355 
M
30073 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowS
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
S 30355
50.2%
M 30073
49.8%

Length

2024-09-15T13:14:34.485743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:34.617788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
s 30355
50.2%
m 30073
49.8%

Most occurring characters

ValueCountFrequency (%)
S 30355
50.2%
M 30073
49.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60428
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 30355
50.2%
M 30073
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 60428
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 30355
50.2%
M 30073
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 30355
50.2%
M 30073
49.8%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
F
30942 
M
29486 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 30942
51.2%
M 29486
48.8%

Length

2024-09-15T13:14:34.803516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:34.947414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 30942
51.2%
m 29486
48.8%

Most occurring characters

ValueCountFrequency (%)
F 30942
51.2%
M 29486
48.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60428
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 30942
51.2%
M 29486
48.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 60428
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 30942
51.2%
M 29486
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 30942
51.2%
M 29486
48.8%

total_children
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.533875
Minimum0
Maximum5
Zeros5624
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:35.080264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.4901648
Coefficient of variation (CV)0.58809719
Kurtosis-1.0395637
Mean2.533875
Median Absolute Deviation (MAD)1
Skewness-0.014983926
Sum153117
Variance2.2205911
MonotonicityNot monotonic
2024-09-15T13:14:35.215159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 12518
20.7%
4 12427
20.6%
3 11921
19.7%
1 11770
19.5%
5 6168
10.2%
0 5624
9.3%
ValueCountFrequency (%)
0 5624
9.3%
1 11770
19.5%
2 12518
20.7%
3 11921
19.7%
4 12427
20.6%
5 6168
10.2%
ValueCountFrequency (%)
5 6168
10.2%
4 12427
20.6%
3 11921
19.7%
2 12518
20.7%
1 11770
19.5%
0 5624
9.3%

education
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Partial High School
18201 
High School Degree
17838 
Bachelors Degree
15994 
Partial College
5284 
Graduate Degree
3111 

Length

Max length19
Median length18
Mean length17.355067
Min length15

Characters and Unicode

Total characters1048732
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPartial High School
2nd rowBachelors Degree
3rd rowPartial High School
4th rowHigh School Degree
5th rowPartial High School

Common Values

ValueCountFrequency (%)
Partial High School 18201
30.1%
High School Degree 17838
29.5%
Bachelors Degree 15994
26.5%
Partial College 5284
 
8.7%
Graduate Degree 3111
 
5.1%

Length

2024-09-15T13:14:35.650333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:35.834425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
degree 36943
23.5%
high 36039
23.0%
school 36039
23.0%
partial 23485
15.0%
bachelors 15994
10.2%
college 5284
 
3.4%
graduate 3111
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 140502
13.4%
96467
9.2%
o 93356
8.9%
h 88072
8.4%
l 86086
8.2%
r 79533
 
7.6%
g 78266
 
7.5%
a 69186
 
6.6%
i 59524
 
5.7%
c 52033
 
5.0%
Other values (11) 205707
19.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 795370
75.8%
Uppercase Letter 156895
 
15.0%
Space Separator 96467
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 140502
17.7%
o 93356
11.7%
h 88072
11.1%
l 86086
10.8%
r 79533
10.0%
g 78266
9.8%
a 69186
8.7%
i 59524
7.5%
c 52033
 
6.5%
t 26596
 
3.3%
Other values (3) 22216
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
D 36943
23.5%
S 36039
23.0%
H 36039
23.0%
P 23485
15.0%
B 15994
10.2%
C 5284
 
3.4%
G 3111
 
2.0%
Space Separator
ValueCountFrequency (%)
96467
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 952265
90.8%
Common 96467
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 140502
14.8%
o 93356
9.8%
h 88072
9.2%
l 86086
9.0%
r 79533
8.4%
g 78266
8.2%
a 69186
7.3%
i 59524
 
6.3%
c 52033
 
5.5%
D 36943
 
3.9%
Other values (10) 168764
17.7%
Common
ValueCountFrequency (%)
96467
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1048732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 140502
13.4%
96467
9.2%
o 93356
8.9%
h 88072
8.4%
l 86086
8.2%
r 79533
 
7.6%
g 78266
 
7.5%
a 69186
 
6.6%
i 59524
 
5.7%
c 52033
 
5.0%
Other values (11) 205707
19.6%

member_card
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Bronze
33807 
Normal
13867 
Golden
7556 
Silver
5198 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters362568
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowSilver
3rd rowNormal
4th rowBronze
5th rowBronze

Common Values

ValueCountFrequency (%)
Bronze 33807
55.9%
Normal 13867
22.9%
Golden 7556
 
12.5%
Silver 5198
 
8.6%

Length

2024-09-15T13:14:36.032260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:36.214608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
bronze 33807
55.9%
normal 13867
22.9%
golden 7556
 
12.5%
silver 5198
 
8.6%

Most occurring characters

ValueCountFrequency (%)
o 55230
15.2%
r 52872
14.6%
e 46561
12.8%
n 41363
11.4%
B 33807
9.3%
z 33807
9.3%
l 26621
7.3%
N 13867
 
3.8%
m 13867
 
3.8%
a 13867
 
3.8%
Other values (5) 30706
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 302140
83.3%
Uppercase Letter 60428
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 55230
18.3%
r 52872
17.5%
e 46561
15.4%
n 41363
13.7%
z 33807
11.2%
l 26621
8.8%
m 13867
 
4.6%
a 13867
 
4.6%
d 7556
 
2.5%
i 5198
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
B 33807
55.9%
N 13867
22.9%
G 7556
 
12.5%
S 5198
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 362568
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 55230
15.2%
r 52872
14.6%
e 46561
12.8%
n 41363
11.4%
B 33807
9.3%
z 33807
9.3%
l 26621
7.3%
N 13867
 
3.8%
m 13867
 
3.8%
a 13867
 
3.8%
Other values (5) 30706
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 55230
15.2%
r 52872
14.6%
e 46561
12.8%
n 41363
11.4%
B 33807
9.3%
z 33807
9.3%
l 26621
7.3%
N 13867
 
3.8%
m 13867
 
3.8%
a 13867
 
3.8%
Other values (5) 30706
8.5%

occupation
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Professional
19915 
Skilled Manual
15995 
Manual
14624 
Management
8805 
Clerical
 
1089

Length

Max length14
Median length12
Mean length10.713841
Min length6

Characters and Unicode

Total characters647416
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSkilled Manual
2nd rowProfessional
3rd rowManual
4th rowManual
5th rowSkilled Manual

Common Values

ValueCountFrequency (%)
Professional 19915
33.0%
Skilled Manual 15995
26.5%
Manual 14624
24.2%
Management 8805
14.6%
Clerical 1089
 
1.8%

Length

2024-09-15T13:14:36.415652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:36.636589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
manual 30619
40.1%
professional 19915
26.1%
skilled 15995
20.9%
management 8805
 
11.5%
clerical 1089
 
1.4%

Most occurring characters

ValueCountFrequency (%)
a 99852
15.4%
l 84702
13.1%
n 68144
10.5%
e 54609
8.4%
o 39830
 
6.2%
s 39830
 
6.2%
M 39424
 
6.1%
i 36999
 
5.7%
u 30619
 
4.7%
r 21004
 
3.2%
Other values (11) 132403
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 554998
85.7%
Uppercase Letter 76423
 
11.8%
Space Separator 15995
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 99852
18.0%
l 84702
15.3%
n 68144
12.3%
e 54609
9.8%
o 39830
 
7.2%
s 39830
 
7.2%
i 36999
 
6.7%
u 30619
 
5.5%
r 21004
 
3.8%
f 19915
 
3.6%
Other values (6) 59494
10.7%
Uppercase Letter
ValueCountFrequency (%)
M 39424
51.6%
P 19915
26.1%
S 15995
20.9%
C 1089
 
1.4%
Space Separator
ValueCountFrequency (%)
15995
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 631421
97.5%
Common 15995
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 99852
15.8%
l 84702
13.4%
n 68144
10.8%
e 54609
8.6%
o 39830
 
6.3%
s 39830
 
6.3%
M 39424
 
6.2%
i 36999
 
5.9%
u 30619
 
4.8%
r 21004
 
3.3%
Other values (10) 116408
18.4%
Common
ValueCountFrequency (%)
15995
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 647416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 99852
15.4%
l 84702
13.1%
n 68144
10.5%
e 54609
8.4%
o 39830
 
6.2%
s 39830
 
6.2%
M 39424
 
6.1%
i 36999
 
5.7%
u 30619
 
4.7%
r 21004
 
3.2%
Other values (11) 132403
20.5%

houseowner
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.1 KiB
True
36510 
False
23918 
ValueCountFrequency (%)
True 36510
60.4%
False 23918
39.6%
2024-09-15T13:14:36.811767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

avg_cars_at home(approx)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
2
18268 
3
16961 
1
13643 
4
7974 
0
3582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Length

2024-09-15T13:14:37.024224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:37.186649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring characters

ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

avg. yearly_income
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
$30K - $50K
19514 
$10K - $30K
12959 
$50K - $70K
10493 
$70K - $90K
7544 
$130K - $150K
3410 
Other values (3)
6508 

Length

Max length13
Median length11
Mean length11.165701
Min length7

Characters and Unicode

Total characters674721
Distinct characters11
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10K - $30K
2nd row$50K - $70K
3rd row$10K - $30K
4th row$30K - $50K
5th row$30K - $50K

Common Values

ValueCountFrequency (%)
$30K - $50K 19514
32.3%
$10K - $30K 12959
21.4%
$50K - $70K 10493
17.4%
$70K - $90K 7544
 
12.5%
$130K - $150K 3410
 
5.6%
$90K - $110K 2737
 
4.5%
$110K - $130K 2590
 
4.3%
$150K + 1181
 
2.0%

Length

2024-09-15T13:14:37.360408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:37.850447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
60428
33.6%
30k 32473
18.0%
50k 30007
16.7%
70k 18037
 
10.0%
10k 12959
 
7.2%
90k 10281
 
5.7%
130k 6000
 
3.3%
110k 5327
 
3.0%
150k 4591
 
2.5%

Most occurring characters

ValueCountFrequency (%)
$ 119675
17.7%
0 119675
17.7%
K 119675
17.7%
119675
17.7%
- 59247
8.8%
3 38473
 
5.7%
5 34598
 
5.1%
1 34204
 
5.1%
7 18037
 
2.7%
9 10281
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 255268
37.8%
Currency Symbol 119675
17.7%
Uppercase Letter 119675
17.7%
Space Separator 119675
17.7%
Dash Punctuation 59247
 
8.8%
Math Symbol 1181
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119675
46.9%
3 38473
 
15.1%
5 34598
 
13.6%
1 34204
 
13.4%
7 18037
 
7.1%
9 10281
 
4.0%
Currency Symbol
ValueCountFrequency (%)
$ 119675
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 119675
100.0%
Space Separator
ValueCountFrequency (%)
119675
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59247
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 555046
82.3%
Latin 119675
 
17.7%

Most frequent character per script

Common
ValueCountFrequency (%)
$ 119675
21.6%
0 119675
21.6%
119675
21.6%
- 59247
10.7%
3 38473
 
6.9%
5 34598
 
6.2%
1 34204
 
6.2%
7 18037
 
3.2%
9 10281
 
1.9%
+ 1181
 
0.2%
Latin
ValueCountFrequency (%)
K 119675
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 674721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
$ 119675
17.7%
0 119675
17.7%
K 119675
17.7%
119675
17.7%
- 59247
8.8%
3 38473
 
5.7%
5 34598
 
5.1%
1 34204
 
5.1%
7 18037
 
2.7%
9 10281
 
1.5%

num_children_at_home
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82935063
Minimum0
Maximum5
Zeros37609
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:38.104654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3034239
Coefficient of variation (CV)1.5716198
Kurtosis1.4672375
Mean0.82935063
Median Absolute Deviation (MAD)0
Skewness1.5542796
Sum50116
Variance1.6989138
MonotonicityNot monotonic
2024-09-15T13:14:38.260703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 37609
62.2%
1 8811
 
14.6%
2 5841
 
9.7%
3 4391
 
7.3%
4 2430
 
4.0%
5 1346
 
2.2%
ValueCountFrequency (%)
0 37609
62.2%
1 8811
 
14.6%
2 5841
 
9.7%
3 4391
 
7.3%
4 2430
 
4.0%
5 1346
 
2.2%
ValueCountFrequency (%)
5 1346
 
2.2%
4 2430
 
4.0%
3 4391
 
7.3%
2 5841
 
9.7%
1 8811
 
14.6%
0 37609
62.2%

avg_cars_at home(approx).1
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
2
18268 
3
16961 
1
13643 
4
7974 
0
3582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Length

2024-09-15T13:14:38.537776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:38.704611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring characters

ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18268
30.2%
3 16961
28.1%
1 13643
22.6%
4 7974
13.2%
0 3582
 
5.9%
Distinct111
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:39.011766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.5219931
Min length3

Characters and Unicode

Total characters454539
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarrington
2nd rowCarrington
3rd rowCarrington
4th rowCarrington
5th rowGolden
ValueCountFrequency (%)
high 2805
 
3.6%
best 2637
 
3.4%
top 1961
 
2.5%
hermanos 1839
 
2.4%
red 1831
 
2.3%
better 1735
 
2.2%
ebony 1729
 
2.2%
tale 1728
 
2.2%
tell 1728
 
2.2%
tri-state 1633
 
2.1%
Other values (115) 58415
74.9%
2024-09-15T13:14:39.559599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 43594
 
9.6%
a 34134
 
7.5%
t 33830
 
7.4%
o 33500
 
7.4%
i 29099
 
6.4%
l 27976
 
6.2%
n 26881
 
5.9%
r 23548
 
5.2%
s 19242
 
4.2%
17613
 
3.9%
Other values (36) 165122
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 350038
77.0%
Uppercase Letter 85255
 
18.8%
Space Separator 17613
 
3.9%
Dash Punctuation 1633
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 43594
12.5%
a 34134
9.8%
t 33830
9.7%
o 33500
9.6%
i 29099
8.3%
l 27976
8.0%
n 26881
7.7%
r 23548
 
6.7%
s 19242
 
5.5%
u 11873
 
3.4%
Other values (13) 66361
19.0%
Uppercase Letter
ValueCountFrequency (%)
B 12675
14.9%
T 10586
12.4%
C 9703
11.4%
H 6883
 
8.1%
S 6806
 
8.0%
P 4566
 
5.4%
F 4519
 
5.3%
R 4067
 
4.8%
G 3730
 
4.4%
E 3707
 
4.3%
Other values (11) 18013
21.1%
Space Separator
ValueCountFrequency (%)
17613
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 435293
95.8%
Common 19246
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 43594
 
10.0%
a 34134
 
7.8%
t 33830
 
7.8%
o 33500
 
7.7%
i 29099
 
6.7%
l 27976
 
6.4%
n 26881
 
6.2%
r 23548
 
5.4%
s 19242
 
4.4%
B 12675
 
2.9%
Other values (34) 150814
34.6%
Common
ValueCountFrequency (%)
17613
91.5%
- 1633
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 454539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 43594
 
9.6%
a 34134
 
7.5%
t 33830
 
7.4%
o 33500
 
7.4%
i 29099
 
6.4%
l 27976
 
6.2%
n 26881
 
5.9%
r 23548
 
5.2%
s 19242
 
4.2%
17613
 
3.9%
Other values (36) 165122
36.3%

SRP
Real number (ℝ)

HIGH CORRELATION 

Distinct315
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1152585
Minimum0.5
Maximum3.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:39.776934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.64
Q11.41
median2.13
Q32.79
95-th percentile3.75
Maximum3.98
Range3.48
Interquartile range (IQR)1.38

Descriptive statistics

Standard deviation0.93282856
Coefficient of variation (CV)0.4409998
Kurtosis-0.89452672
Mean2.1152585
Median Absolute Deviation (MAD)0.69
Skewness0.13792455
Sum127820.84
Variance0.87016912
MonotonicityNot monotonic
2024-09-15T13:14:39.973241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 585
 
1.0%
2.47 558
 
0.9%
2.59 465
 
0.8%
1.68 453
 
0.7%
2.7 414
 
0.7%
1.55 411
 
0.7%
2.95 405
 
0.7%
2.76 394
 
0.7%
1.74 386
 
0.6%
2.13 382
 
0.6%
Other values (305) 55975
92.6%
ValueCountFrequency (%)
0.5 75
 
0.1%
0.51 228
0.4%
0.52 105
 
0.2%
0.53 324
0.5%
0.54 178
0.3%
0.55 238
0.4%
0.56 184
0.3%
0.57 347
0.6%
0.58 162
0.3%
0.59 188
0.3%
ValueCountFrequency (%)
3.98 205
0.3%
3.97 127
 
0.2%
3.96 114
 
0.2%
3.95 321
0.5%
3.94 80
 
0.1%
3.93 149
0.2%
3.92 65
 
0.1%
3.91 206
0.3%
3.9 48
 
0.1%
3.89 158
0.3%

gross_weight
Real number (ℝ)

HIGH CORRELATION 

Distinct376
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.806433
Minimum6
Maximum21.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:40.161691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.96
Q19.7
median13.6
Q317.7
95-th percentile21.2
Maximum21.9
Range15.9
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6226928
Coefficient of variation (CV)0.33482165
Kurtosis-1.2317722
Mean13.806433
Median Absolute Deviation (MAD)4
Skewness0.092975483
Sum834295.14
Variance21.369288
MonotonicityNot monotonic
2024-09-15T13:14:40.483058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.1 704
 
1.2%
19.9 624
 
1.0%
14.7 621
 
1.0%
17.2 588
 
1.0%
20.9 563
 
0.9%
13.7 558
 
0.9%
13.2 551
 
0.9%
16.1 551
 
0.9%
14.5 542
 
0.9%
18.7 535
 
0.9%
Other values (366) 54591
90.3%
ValueCountFrequency (%)
6 42
0.1%
6.03 32
0.1%
6.04 40
0.1%
6.06 51
0.1%
6.09 33
0.1%
6.11 78
0.1%
6.12 39
0.1%
6.13 41
0.1%
6.14 67
0.1%
6.15 40
0.1%
ValueCountFrequency (%)
21.9 500
0.8%
21.8 530
0.9%
21.7 509
0.8%
21.6 315
0.5%
21.5 299
0.5%
21.4 293
0.5%
21.3 453
0.7%
21.2 497
0.8%
21.1 293
0.5%
21 407
0.7%

net_weight
Real number (ℝ)

HIGH CORRELATION 

Distinct332
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.796289
Minimum3.05
Maximum20.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:40.736415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.05
5-th percentile4.94
Q17.71
median11.6
Q316
95-th percentile19.2
Maximum20.8
Range17.75
Interquartile range (IQR)8.29

Descriptive statistics

Standard deviation4.6829862
Coefficient of variation (CV)0.39698808
Kurtosis-1.1933676
Mean11.796289
Median Absolute Deviation (MAD)4.1
Skewness0.1066776
Sum712826.16
Variance21.93036
MonotonicityNot monotonic
2024-09-15T13:14:40.970662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.2 1001
 
1.7%
11.6 951
 
1.6%
10.6 904
 
1.5%
11.1 814
 
1.3%
16.7 809
 
1.3%
18.7 778
 
1.3%
19.7 777
 
1.3%
15.2 774
 
1.3%
11.3 769
 
1.3%
16.6 717
 
1.2%
Other values (322) 52134
86.3%
ValueCountFrequency (%)
3.05 51
0.1%
3.09 33
0.1%
3.11 75
0.1%
3.13 36
0.1%
3.28 36
0.1%
3.3 44
0.1%
3.38 38
0.1%
3.4 32
0.1%
3.42 31
0.1%
3.59 32
0.1%
ValueCountFrequency (%)
20.8 89
 
0.1%
20.7 251
 
0.4%
20.6 149
 
0.2%
20.5 33
 
0.1%
20.3 74
 
0.1%
20.2 307
 
0.5%
20.1 192
 
0.3%
20 139
 
0.2%
19.8 294
 
0.5%
19.7 777
1.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
1
33759 
0
26669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 33759
55.9%
0 26669
44.1%

Length

2024-09-15T13:14:41.173660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:41.308949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 33759
55.9%
0 26669
44.1%

Most occurring characters

ValueCountFrequency (%)
1 33759
55.9%
0 26669
44.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33759
55.9%
0 26669
44.1%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33759
55.9%
0 26669
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33759
55.9%
0 26669
44.1%

low_fat
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
0
39252 
1
21176 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 39252
65.0%
1 21176
35.0%

Length

2024-09-15T13:14:41.463898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:41.624666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 39252
65.0%
1 21176
35.0%

Most occurring characters

ValueCountFrequency (%)
0 39252
65.0%
1 21176
35.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39252
65.0%
1 21176
35.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39252
65.0%
1 21176
35.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39252
65.0%
1 21176
35.0%

units_per_case
Real number (ℝ)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.860694
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:41.772463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median19
Q328
95-th percentile34
Maximum36
Range35
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.258555
Coefficient of variation (CV)0.54391185
Kurtosis-1.2518663
Mean18.860694
Median Absolute Deviation (MAD)9
Skewness-0.083626952
Sum1139714
Variance105.23795
MonotonicityNot monotonic
2024-09-15T13:14:42.007762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
29 2294
 
3.8%
6 2285
 
3.8%
33 2217
 
3.7%
31 2107
 
3.5%
23 2085
 
3.5%
30 2073
 
3.4%
26 2013
 
3.3%
25 1993
 
3.3%
5 1958
 
3.2%
9 1950
 
3.2%
Other values (26) 39453
65.3%
ValueCountFrequency (%)
1 952
1.6%
2 1558
2.6%
3 1877
3.1%
4 1573
2.6%
5 1958
3.2%
6 2285
3.8%
7 1480
2.4%
8 1364
2.3%
9 1950
3.2%
10 1387
2.3%
ValueCountFrequency (%)
36 749
 
1.2%
35 1577
2.6%
34 1740
2.9%
33 2217
3.7%
32 1607
2.7%
31 2107
3.5%
30 2073
3.4%
29 2294
3.8%
28 1536
2.5%
27 1632
2.7%

store_type
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Supermarket
26192 
Deluxe Supermarket
22954 
Gourmet Supermarket
6503 
Mid-Size Grocery
2846 
Small Grocery
 
1933

Length

Max length19
Median length18
Mean length14.819388
Min length11

Characters and Unicode

Total characters895506
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeluxe Supermarket
2nd rowDeluxe Supermarket
3rd rowDeluxe Supermarket
4th rowDeluxe Supermarket
5th rowDeluxe Supermarket

Common Values

ValueCountFrequency (%)
Supermarket 26192
43.3%
Deluxe Supermarket 22954
38.0%
Gourmet Supermarket 6503
 
10.8%
Mid-Size Grocery 2846
 
4.7%
Small Grocery 1933
 
3.2%

Length

2024-09-15T13:14:42.211796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:42.491223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
supermarket 55649
58.8%
deluxe 22954
24.2%
gourmet 6503
 
6.9%
grocery 4779
 
5.0%
mid-size 2846
 
3.0%
small 1933
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 171334
19.1%
r 127359
14.2%
u 85106
9.5%
m 64085
 
7.2%
t 62152
 
6.9%
S 60428
 
6.7%
a 57582
 
6.4%
p 55649
 
6.2%
k 55649
 
6.2%
34236
 
3.8%
Other values (12) 121926
13.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 760914
85.0%
Uppercase Letter 97510
 
10.9%
Space Separator 34236
 
3.8%
Dash Punctuation 2846
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 171334
22.5%
r 127359
16.7%
u 85106
11.2%
m 64085
 
8.4%
t 62152
 
8.2%
a 57582
 
7.6%
p 55649
 
7.3%
k 55649
 
7.3%
l 26820
 
3.5%
x 22954
 
3.0%
Other values (6) 32224
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
S 60428
62.0%
D 22954
 
23.5%
G 11282
 
11.6%
M 2846
 
2.9%
Space Separator
ValueCountFrequency (%)
34236
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 858424
95.9%
Common 37082
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 171334
20.0%
r 127359
14.8%
u 85106
9.9%
m 64085
 
7.5%
t 62152
 
7.2%
S 60428
 
7.0%
a 57582
 
6.7%
p 55649
 
6.5%
k 55649
 
6.5%
l 26820
 
3.1%
Other values (10) 92260
10.7%
Common
ValueCountFrequency (%)
34236
92.3%
- 2846
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 895506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 171334
19.1%
r 127359
14.2%
u 85106
9.5%
m 64085
 
7.2%
t 62152
 
6.9%
S 60428
 
6.7%
a 57582
 
6.4%
p 55649
 
6.2%
k 55649
 
6.2%
34236
 
3.8%
Other values (12) 121926
13.6%

store_city
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Tacoma
5704 
Salem
5478 
Portland
5150 
Seattle
5051 
Hidalgo
4761 
Other values (14)
34284 

Length

Max length13
Median length10
Mean length7.9045641
Min length5

Characters and Unicode

Total characters477657
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalem
2nd rowSalem
3rd rowSalem
4th rowSalem
5th rowSalem

Common Values

ValueCountFrequency (%)
Tacoma 5704
9.4%
Salem 5478
 
9.1%
Portland 5150
 
8.5%
Seattle 5051
 
8.4%
Hidalgo 4761
 
7.9%
Merida 4498
 
7.4%
Spokane 4453
 
7.4%
Beverly Hills 4151
 
6.9%
Los Angeles 3960
 
6.6%
Bremerton 3451
 
5.7%
Other values (9) 13771
22.8%

Length

2024-09-15T13:14:42.680525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tacoma 5704
 
8.1%
salem 5478
 
7.7%
portland 5150
 
7.3%
seattle 5051
 
7.1%
hidalgo 4761
 
6.7%
merida 4498
 
6.4%
spokane 4453
 
6.3%
beverly 4151
 
5.9%
hills 4151
 
5.9%
los 3960
 
5.6%
Other values (13) 23360
33.0%

Most occurring characters

ValueCountFrequency (%)
a 60687
 
12.7%
e 53145
 
11.1%
l 40220
 
8.4%
o 37479
 
7.8%
r 28508
 
6.0%
n 22675
 
4.7%
i 21475
 
4.5%
t 20678
 
4.3%
c 17993
 
3.8%
m 17696
 
3.7%
Other values (27) 157101
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 396651
83.0%
Uppercase Letter 70717
 
14.8%
Space Separator 10289
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 60687
15.3%
e 53145
13.4%
l 40220
10.1%
o 37479
9.4%
r 28508
 
7.2%
n 22675
 
5.7%
i 21475
 
5.4%
t 20678
 
5.2%
c 17993
 
4.5%
m 17696
 
4.5%
Other values (13) 76095
19.2%
Uppercase Letter
ValueCountFrequency (%)
S 15765
22.3%
H 8912
12.6%
B 8313
11.8%
M 5893
 
8.3%
T 5704
 
8.1%
A 5466
 
7.7%
P 5150
 
7.3%
V 3964
 
5.6%
L 3960
 
5.6%
C 3747
 
5.3%
Other values (3) 3843
 
5.4%
Space Separator
ValueCountFrequency (%)
10289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 467368
97.8%
Common 10289
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 60687
13.0%
e 53145
 
11.4%
l 40220
 
8.6%
o 37479
 
8.0%
r 28508
 
6.1%
n 22675
 
4.9%
i 21475
 
4.6%
t 20678
 
4.4%
c 17993
 
3.8%
m 17696
 
3.8%
Other values (26) 146812
31.4%
Common
ValueCountFrequency (%)
10289
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 60687
 
12.7%
e 53145
 
11.1%
l 40220
 
8.4%
o 37479
 
7.8%
r 28508
 
6.0%
n 22675
 
4.7%
i 21475
 
4.5%
t 20678
 
4.3%
c 17993
 
3.8%
m 17696
 
3.7%
Other values (27) 157101
32.9%

store_state
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
WA
19370 
OR
10628 
CA
8894 
Zacatecas
7113 
Yucatan
4498 
Other values (5)
9925 

Length

Max length9
Median length2
Mean length3.6422519
Min length2

Characters and Unicode

Total characters220094
Distinct characters25
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOR
2nd rowOR
3rd rowOR
4th rowOR
5th rowOR

Common Values

ValueCountFrequency (%)
WA 19370
32.1%
OR 10628
17.6%
CA 8894
14.7%
Zacatecas 7113
 
11.8%
Yucatan 4498
 
7.4%
BC 3964
 
6.6%
Veracruz 2621
 
4.3%
Guerrero 1506
 
2.5%
DF 1395
 
2.3%
Jalisco 439
 
0.7%

Length

2024-09-15T13:14:42.909062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:43.132447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
wa 19370
32.1%
or 10628
17.6%
ca 8894
14.7%
zacatecas 7113
 
11.8%
yucatan 4498
 
7.4%
bc 3964
 
6.6%
veracruz 2621
 
4.3%
guerrero 1506
 
2.5%
df 1395
 
2.3%
jalisco 439
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 33395
15.2%
A 28264
12.8%
c 21784
9.9%
W 19370
 
8.8%
C 12858
 
5.8%
e 12746
 
5.8%
t 11611
 
5.3%
O 10628
 
4.8%
R 10628
 
4.8%
r 9760
 
4.4%
Other values (15) 49050
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 115415
52.4%
Uppercase Letter 104679
47.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 28264
27.0%
W 19370
18.5%
C 12858
12.3%
O 10628
 
10.2%
R 10628
 
10.2%
Z 7113
 
6.8%
Y 4498
 
4.3%
B 3964
 
3.8%
V 2621
 
2.5%
G 1506
 
1.4%
Other values (3) 3229
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
a 33395
28.9%
c 21784
18.9%
e 12746
 
11.0%
t 11611
 
10.1%
r 9760
 
8.5%
u 8625
 
7.5%
s 7552
 
6.5%
n 4498
 
3.9%
z 2621
 
2.3%
o 1945
 
1.7%
Other values (2) 878
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 220094
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 33395
15.2%
A 28264
12.8%
c 21784
9.9%
W 19370
 
8.8%
C 12858
 
5.8%
e 12746
 
5.8%
t 11611
 
5.3%
O 10628
 
4.8%
R 10628
 
4.8%
r 9760
 
4.4%
Other values (15) 49050
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 33395
15.2%
A 28264
12.8%
c 21784
9.9%
W 19370
 
8.8%
C 12858
 
5.8%
e 12746
 
5.8%
t 11611
 
5.3%
O 10628
 
4.8%
R 10628
 
4.8%
r 9760
 
4.4%
Other values (15) 49050
22.3%

store_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27988.477
Minimum20319
Maximum39696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:43.319842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20319
5-th percentile20319
Q123593
median27694
Q330797
95-th percentile39696
Maximum39696
Range19377
Interquartile range (IQR)7204

Descriptive statistics

Standard deviation5701.0221
Coefficient of variation (CV)0.20369175
Kurtosis-0.93715952
Mean27988.477
Median Absolute Deviation (MAD)4101
Skewness0.38667854
Sum1.6912877 × 109
Variance32501653
MonotonicityNot monotonic
2024-09-15T13:14:43.494901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
33858 5704
 
9.4%
27694 5478
 
9.1%
20319 5150
 
8.5%
21215 5051
 
8.4%
30797 4498
 
7.4%
30268 4453
 
7.4%
23688 4151
 
6.9%
23598 3960
 
6.6%
30584 3890
 
6.4%
39696 3451
 
5.7%
Other values (10) 14642
24.2%
ValueCountFrequency (%)
20319 5150
8.5%
21215 5051
8.4%
22478 783
 
1.3%
23112 3384
5.6%
23593 1506
 
2.5%
23598 3960
6.6%
23688 4151
6.9%
23759 2352
3.9%
24597 439
 
0.7%
27694 5478
9.1%
ValueCountFrequency (%)
39696 3451
5.7%
38382 871
 
1.4%
36509 1395
 
2.3%
34791 2621
4.3%
34452 580
 
1.0%
33858 5704
9.4%
30797 4498
7.4%
30584 3890
6.4%
30268 4453
7.4%
28206 711
 
1.2%

grocery_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19133.8
Minimum13305
Maximum30351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:43.646679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum13305
5-th percentile13305
Q116232
median18670
Q322123
95-th percentile26354
Maximum30351
Range17046
Interquartile range (IQR)5891

Descriptive statistics

Standard deviation3987.3957
Coefficient of variation (CV)0.20839539
Kurtosis-0.54216379
Mean19133.8
Median Absolute Deviation (MAD)3333
Skewness0.3853139
Sum1.1562172 × 109
Variance15899325
MonotonicityNot monotonic
2024-09-15T13:14:43.829431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
22123 5704
 
9.4%
18670 5478
 
9.1%
16232 5150
 
8.5%
13305 5051
 
8.4%
20141 4498
 
7.4%
22063 4453
 
7.4%
15337 4151
 
6.9%
14210 3960
 
6.6%
21938 3890
 
6.4%
24390 3451
 
5.7%
Other values (10) 14642
24.2%
ValueCountFrequency (%)
13305 5051
8.4%
14210 3960
6.6%
15012 439
 
0.7%
15321 783
 
1.3%
15337 4151
6.9%
16232 5150
8.5%
16418 3384
5.6%
16844 2352
3.9%
17475 1506
 
2.5%
18670 5478
9.1%
ValueCountFrequency (%)
30351 871
 
1.4%
27463 580
 
1.0%
26354 2621
4.3%
24390 3451
5.7%
22450 1395
 
2.3%
22271 711
 
1.2%
22123 5704
9.4%
22063 4453
7.4%
21938 3890
6.4%
20141 4498
7.4%

frozen_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5312.8526
Minimum2452
Maximum9184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:43.987525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2452
5-th percentile2452
Q14746
median5062
Q35751
95-th percentile9184
Maximum9184
Range6732
Interquartile range (IQR)1005

Descriptive statistics

Standard deviation1575.9073
Coefficient of variation (CV)0.29662168
Kurtosis0.60528444
Mean5312.8526
Median Absolute Deviation (MAD)571
Skewness0.56104096
Sum3.2104505 × 108
Variance2483483.7
MonotonicityNot monotonic
2024-09-15T13:14:44.229383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7041 5704
 
9.4%
5415 5478
 
9.1%
2452 5150
 
8.5%
4746 5051
 
8.4%
6393 4498
 
7.4%
4923 4453
 
7.4%
5011 4151
 
6.9%
5633 3960
 
6.6%
5188 3890
 
6.4%
9184 3451
 
5.7%
Other values (10) 14642
24.2%
ValueCountFrequency (%)
2452 5150
8.5%
3561 711
 
1.2%
3671 1506
 
2.5%
4016 3384
5.6%
4149 2352
3.9%
4193 580
 
1.0%
4294 783
 
1.3%
4746 5051
8.4%
4819 871
 
1.4%
4923 4453
7.4%
ValueCountFrequency (%)
9184 3451
5.7%
8435 1395
 
2.3%
7041 5704
9.4%
6393 4498
7.4%
5751 439
 
0.7%
5633 3960
6.6%
5415 5478
9.1%
5188 3890
6.4%
5062 2621
4.3%
5011 4151
6.9%

meat_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3541.8463
Minimum1635
Maximum6122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:44.459565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1635
5-th percentile1635
Q13164
median3375
Q33834
95-th percentile6122
Maximum6122
Range4487
Interquartile range (IQR)670

Descriptive statistics

Standard deviation1050.4716
Coefficient of variation (CV)0.29658871
Kurtosis0.60484808
Mean3541.8463
Median Absolute Deviation (MAD)380
Skewness0.56123776
Sum2.1402669 × 108
Variance1103490.7
MonotonicityNot monotonic
2024-09-15T13:14:44.637582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4694 5704
 
9.4%
3610 5478
 
9.1%
1635 5150
 
8.5%
3164 5051
 
8.4%
4262 4498
 
7.4%
3282 4453
 
7.4%
3340 4151
 
6.9%
3755 3960
 
6.6%
3458 3890
 
6.4%
6122 3451
 
5.7%
Other values (10) 14642
24.2%
ValueCountFrequency (%)
1635 5150
8.5%
2374 711
 
1.2%
2447 1506
 
2.5%
2678 3384
5.6%
2766 2352
3.9%
2795 580
 
1.0%
2863 783
 
1.3%
3164 5051
8.4%
3213 871
 
1.4%
3282 4453
7.4%
ValueCountFrequency (%)
6122 3451
5.7%
5624 1395
 
2.3%
4694 5704
9.4%
4262 4498
7.4%
3834 439
 
0.7%
3755 3960
6.6%
3610 5478
9.1%
3458 3890
6.4%
3375 2621
4.3%
3340 4151
6.9%

coffee_bar
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
1
37021 
0
23407 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 37021
61.3%
0 23407
38.7%

Length

2024-09-15T13:14:44.822550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:44.995141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 37021
61.3%
0 23407
38.7%

Most occurring characters

ValueCountFrequency (%)
1 37021
61.3%
0 23407
38.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37021
61.3%
0 23407
38.7%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37021
61.3%
0 23407
38.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37021
61.3%
0 23407
38.7%

video_store
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
0
39027 
1
21401 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 39027
64.6%
1 21401
35.4%

Length

2024-09-15T13:14:45.186619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:45.337547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 39027
64.6%
1 21401
35.4%

Most occurring characters

ValueCountFrequency (%)
0 39027
64.6%
1 21401
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39027
64.6%
1 21401
35.4%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39027
64.6%
1 21401
35.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39027
64.6%
1 21401
35.4%

salad_bar
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
1
35529 
0
24899 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Length

2024-09-15T13:14:45.477197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:45.620158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring characters

ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

prepared_food
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
1
35529 
0
24899 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Length

2024-09-15T13:14:45.801411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:46.180694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring characters

ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35529
58.8%
0 24899
41.2%

florist
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
1
33997 
0
26431 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 33997
56.3%
0 26431
43.7%

Length

2024-09-15T13:14:46.357876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T13:14:46.493930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 33997
56.3%
0 26431
43.7%

Most occurring characters

ValueCountFrequency (%)
1 33997
56.3%
0 26431
43.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33997
56.3%
0 26431
43.7%

Most occurring scripts

ValueCountFrequency (%)
Common 60428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33997
56.3%
0 26431
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33997
56.3%
0 26431
43.7%

media_type
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.2 KiB
Daily Paper, Radio
6820 
Product Attachment
5371 
Daily Paper, Radio, TV
5284 
Daily Paper
5119 
Street Handout
5069 
Other values (8)
32765 

Length

Max length23
Median length18
Mean length14.725111
Min length2

Characters and Unicode

Total characters889809
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDaily Paper, Radio
2nd rowDaily Paper, Radio
3rd rowDaily Paper, Radio
4th rowIn-Store Coupon
5th rowRadio

Common Values

ValueCountFrequency (%)
Daily Paper, Radio 6820
11.3%
Product Attachment 5371
8.9%
Daily Paper, Radio, TV 5284
8.7%
Daily Paper 5119
8.5%
Street Handout 5069
8.4%
Radio 4980
8.2%
Sunday Paper 4859
8.0%
In-Store Coupon 4495
7.4%
Sunday Paper, Radio 4050
 
6.7%
Cash Register Handout 4002
 
6.6%
Other values (3) 10379
17.2%

Length

2024-09-15T13:14:46.644608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
paper 29478
20.4%
radio 24480
16.9%
daily 17223
11.9%
sunday 12255
8.5%
tv 12206
8.5%
handout 9071
 
6.3%
product 5371
 
3.7%
attachment 5371
 
3.7%
street 5069
 
3.5%
in-store 4495
 
3.1%
Other values (5) 19413
13.4%

Most occurring characters

ValueCountFrequency (%)
a 105337
 
11.8%
84004
 
9.4%
e 57486
 
6.5%
o 52407
 
5.9%
d 51177
 
5.8%
t 49190
 
5.5%
i 49162
 
5.5%
r 48415
 
5.4%
n 35687
 
4.0%
P 34849
 
3.9%
Other values (23) 322095
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 612047
68.8%
Uppercase Letter 161133
 
18.1%
Space Separator 84004
 
9.4%
Other Punctuation 28130
 
3.2%
Dash Punctuation 4495
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 105337
17.2%
e 57486
9.4%
o 52407
8.6%
d 51177
8.4%
t 49190
8.0%
i 49162
8.0%
r 48415
7.9%
n 35687
 
5.8%
u 34649
 
5.7%
p 33973
 
5.6%
Other values (8) 94564
15.5%
Uppercase Letter
ValueCountFrequency (%)
P 34849
21.6%
R 28482
17.7%
S 21819
13.5%
D 17223
10.7%
T 12206
 
7.6%
V 12206
 
7.6%
H 9071
 
5.6%
C 8497
 
5.3%
A 5371
 
3.3%
I 4495
 
2.8%
Other values (2) 6914
 
4.3%
Space Separator
ValueCountFrequency (%)
84004
100.0%
Other Punctuation
ValueCountFrequency (%)
, 28130
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4495
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 773180
86.9%
Common 116629
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 105337
13.6%
e 57486
 
7.4%
o 52407
 
6.8%
d 51177
 
6.6%
t 49190
 
6.4%
i 49162
 
6.4%
r 48415
 
6.3%
n 35687
 
4.6%
P 34849
 
4.5%
u 34649
 
4.5%
Other values (20) 254821
33.0%
Common
ValueCountFrequency (%)
84004
72.0%
, 28130
 
24.1%
- 4495
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 889809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 105337
 
11.8%
84004
 
9.4%
e 57486
 
6.5%
o 52407
 
5.9%
d 51177
 
5.8%
t 49190
 
5.5%
i 49162
 
5.5%
r 48415
 
5.4%
n 35687
 
4.0%
P 34849
 
3.9%
Other values (23) 322095
36.2%

cost
Real number (ℝ)

Distinct328
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.262366
Minimum50.79
Maximum149.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.2 KiB
2024-09-15T13:14:46.870989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50.79
5-th percentile53.82
Q169.65
median98.52
Q3126.62
95-th percentile145.41
Maximum149.75
Range98.96
Interquartile range (IQR)56.97

Descriptive statistics

Standard deviation30.011257
Coefficient of variation (CV)0.30234275
Kurtosis-1.2658305
Mean99.262366
Median Absolute Deviation (MAD)28.1
Skewness0.037238951
Sum5998226.3
Variance900.67556
MonotonicityNot monotonic
2024-09-15T13:14:47.082412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.84 839
 
1.4%
69.63 763
 
1.3%
59.86 726
 
1.2%
81.79 698
 
1.2%
131.81 619
 
1.0%
126.62 593
 
1.0%
92.57 576
 
1.0%
69.47 539
 
0.9%
99.38 532
 
0.9%
91.28 530
 
0.9%
Other values (318) 54013
89.4%
ValueCountFrequency (%)
50.79 232
0.4%
51 241
0.4%
51.12 432
0.7%
51.16 50
 
0.1%
51.27 120
 
0.2%
51.47 133
 
0.2%
52.06 328
0.5%
52.42 31
 
0.1%
52.77 72
 
0.1%
52.97 170
 
0.3%
ValueCountFrequency (%)
149.75 149
 
0.2%
149.08 394
0.7%
148.87 150
 
0.2%
148.62 329
0.5%
147.82 344
0.6%
147.35 125
 
0.2%
147.18 177
 
0.3%
147.17 214
0.4%
146.72 529
0.9%
146.41 144
 
0.2%

Interactions

2024-09-15T13:14:28.017874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:51.631859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:54.682130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:57.908952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:01.177452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:04.257970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:07.381316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:10.500965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:13.779273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:17.015889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:19.265522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:21.466546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:23.619583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:25.911812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:28.147850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:51.823513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:54.842333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:58.082000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:01.312900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:04.410557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:07.558188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:10.647483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:14.430524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:17.180150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:19.399101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:21.596974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:23.752581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:26.069085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:28.283287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:51.990274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:55.015664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:58.258133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:01.443120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:04.610094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:07.823734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:10.908076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:14.754441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:17.321873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:19.564057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:21.773182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:23.898650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:26.206333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:28.441008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:52.128896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:55.170782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:58.386325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:01.604246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:04.914009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:08.165732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:11.277839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:15.088698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:17.471562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:19.710460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:21.917163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:24.069804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:26.356065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:28.563694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:52.272040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:55.329475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:58.531523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:01.836287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:05.188171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:08.429035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:11.646091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:15.348879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:17.638795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:19.857794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:22.082309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:24.214251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:26.496678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:28.696802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:52.418680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:55.509294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:59.081078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:02.178255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:05.504465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:08.792005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:11.962919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:15.518883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:17.784645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:19.993719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:22.222941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:24.344094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:26.620474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:28.841031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:52.573956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:55.840423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:59.355922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:02.485323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:05.823262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:09.135527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:12.213329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:15.716563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:17.940885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:20.159356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:22.369107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:24.485542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:26.767501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:28.982834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:52.857437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:56.176166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:59.745133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:02.875510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:06.200504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:09.332448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:12.416471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:15.869499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:18.103979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:20.310955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:22.523650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:24.630954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:26.953404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:29.122341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:53.194703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:56.560903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:00.122325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:03.163402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:06.402580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:09.492733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:12.617383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:16.009537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:18.241011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:20.466318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:22.681781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:24.784428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:27.102155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:29.283458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:53.530396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:56.929010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:00.273270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:03.362504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:06.595505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:09.693899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:12.823586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:16.156785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:18.420505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:20.660645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:22.852018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:24.972014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:27.268030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:29.435344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:53.887191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:57.218818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:00.481029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:03.577073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:06.813557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:09.844915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:13.022290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:16.329906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:18.608183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:20.856175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:23.024864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:25.359480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:27.412853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:29.598010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:54.190519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:57.419540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:00.670822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:03.742358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:07.004946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:10.026485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:13.211729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:16.494098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:18.796721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:21.032420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:23.176963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:25.511104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:27.583582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:29.735189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:54.367330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:57.593247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:00.869661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:03.918083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:07.137006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:10.182194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:13.362334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:16.663289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:18.978482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:21.189889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:23.339595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:25.651295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:27.713877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:29.861278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:54.506753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:13:57.747357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:01.016753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:04.088861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:07.256294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:10.336568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:13.541086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:16.826843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:19.123679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:21.326568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:23.478589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:25.781779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T13:14:27.854087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-15T13:14:47.354017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
SRPavg. yearly_incomeavg_cars_at home(approx)avg_cars_at home(approx).1coffee_barcosteducationfloristfood_categoryfood_departmentfood_familyfrozen_sqftgendergrocery_sqftgross_weighthouseownerlow_fatmarital_statusmeat_sqftmedia_typemember_cardnet_weightnum_children_at_homeoccupationprepared_foodpromotion_namerecyclable_packagesalad_barsales_countrystore_citystore_cost(in millions)store_sales(in millions)store_sqftstore_statestore_typetotal_childrenunit_sales(in millions)units_per_casevideo_store
SRP1.0000.0000.0050.0050.0000.0020.0070.0000.1710.1070.0960.0040.000-0.0010.0480.0050.0790.0000.0040.0030.0000.043-0.0010.0000.0000.0070.0930.0000.0000.0000.8270.8550.0010.0020.005-0.000-0.003-0.0150.000
avg. yearly_income0.0001.0000.3360.3360.0430.0260.5240.0420.0000.0000.0000.0550.0550.0610.0040.1740.0000.0520.0550.0340.5240.0000.0490.4330.0280.0660.0070.0280.0400.0760.0030.0000.0560.0540.0440.0530.0180.0000.051
avg_cars_at home(approx)0.0050.3361.0001.0000.0280.0340.2320.0370.0000.0040.0040.0560.0330.0610.0000.0790.0000.0730.0560.0310.3440.0000.1010.1620.0430.0570.0000.0430.0580.0870.0000.0020.0550.0630.0390.0700.0150.0000.045
avg_cars_at home(approx).10.0050.3361.0001.0000.0280.0340.2320.0370.0000.0040.0040.0560.0330.0610.0000.0790.0000.0730.0560.0310.3440.0000.1010.1620.0430.0570.0000.0430.0580.0870.0000.0020.0550.0630.0390.0700.0150.0000.045
coffee_bar0.0000.0430.0280.0281.0000.2130.0260.6310.0000.0000.0090.6870.0300.5430.0000.0000.0000.0210.6870.2120.0430.0090.0300.0290.5310.4670.0000.5310.2210.9750.0280.0290.5780.5310.8270.0210.1010.0070.589
cost0.0020.0260.0340.0340.2131.0000.0210.1860.0000.0000.000-0.0750.039-0.0300.0020.0270.0040.010-0.0750.2680.0340.002-0.0030.0340.2010.4710.0000.2010.1980.243-0.007-0.007-0.0430.1920.149-0.004-0.014-0.0030.193
education0.0070.5240.2320.2320.0260.0211.0000.0280.0000.0040.0000.0460.0170.0550.0050.0660.0060.0250.0460.0310.3570.0060.0520.4360.0130.0600.0120.0130.0180.0650.0060.0090.0480.0520.0270.0540.0170.0000.029
florist0.0000.0420.0370.0370.6310.1860.0281.0000.0000.0000.0010.8440.0080.5430.0000.0000.0000.0060.8440.1710.0380.0000.0260.0280.6420.4490.0000.6420.2560.9760.0370.0390.8000.6720.8620.0130.1400.0000.653
food_category0.1710.0000.0000.0000.0000.0000.0000.0001.0000.8820.9570.0000.0000.0000.1660.0000.4510.0000.0000.0060.0000.1640.0000.0000.0000.0000.1990.0000.0000.0000.0630.0780.0000.0000.0000.0000.0060.1800.009
food_department0.1070.0000.0040.0040.0000.0000.0040.0000.8821.0000.9630.0000.0200.0000.1140.0000.4390.0050.0000.0000.0000.1170.0030.0000.0060.0000.1160.0060.0000.0000.0400.0540.0000.0000.0000.0000.0070.1280.005
food_family0.0960.0000.0040.0040.0090.0000.0000.0010.9570.9631.0000.0000.0040.0000.0930.0030.4300.0120.0000.0000.0000.0850.0070.0000.0030.0000.0380.0030.0040.0000.0410.0540.0000.0000.0080.0000.0000.0830.010
frozen_sqft0.0040.0550.0560.0560.687-0.0750.0460.8440.0000.0000.0001.0000.0390.467-0.0040.0460.0090.0501.0000.2140.048-0.004-0.0150.0550.6640.4120.0090.6640.6840.9940.0240.0240.7500.7410.612-0.0220.031-0.0000.654
gender0.0000.0550.0330.0330.0300.0390.0170.0080.0000.0200.0040.0391.0000.0570.0010.0000.0110.0240.0390.0270.0390.0000.0500.0310.0270.0550.0000.0270.0600.0980.0000.0000.0590.0960.0280.0590.0000.0000.011
grocery_sqft-0.0010.0610.0610.0610.543-0.0300.0550.5430.0000.0000.0000.4670.0571.000-0.0040.0620.0000.0630.4670.2020.055-0.0030.0250.0590.6600.4230.0000.6600.6940.9510.0120.0100.9000.6750.6060.0170.0190.0030.732
gross_weight0.0480.0040.0000.0000.0000.0020.0050.0000.1660.1140.093-0.0040.001-0.0041.0000.0000.0960.000-0.0040.0000.0010.989-0.0020.0000.0020.0000.0900.0020.0060.0000.0390.040-0.0050.0000.0020.0000.001-0.0090.009
houseowner0.0050.1740.0790.0790.0000.0270.0660.0000.0000.0000.0030.0460.0000.0620.0001.0000.0000.2820.0460.0290.1200.0000.2380.1040.0110.0590.0000.0110.0140.0730.0000.0050.0490.0540.0170.0570.0090.0000.011
low_fat0.0790.0000.0000.0000.0000.0040.0060.0000.4510.4390.4300.0090.0110.0000.0960.0001.0000.0000.0090.0070.0000.0770.0000.0000.0000.0000.0260.0000.0130.0150.0060.0150.0100.0140.0060.0000.0000.0800.002
marital_status0.0000.0520.0730.0730.0210.0100.0250.0060.0000.0050.0120.0500.0240.0630.0000.2820.0001.0000.0500.0250.2360.0000.7830.0340.0210.0720.0000.0210.0190.0810.0000.0070.0580.0490.0340.0610.0150.0000.007
meat_sqft0.0040.0550.0560.0560.687-0.0750.0460.8440.0000.0000.0001.0000.0390.467-0.0040.0460.0090.0501.0000.2140.048-0.004-0.0150.0550.6640.4120.0090.6640.6840.9940.0240.0240.7500.7410.612-0.0220.031-0.0000.654
media_type0.0030.0340.0310.0310.2120.2680.0310.1710.0060.0000.0000.2140.0270.2020.0000.0290.0070.0250.2141.0000.0320.0000.0340.0340.2140.4620.0000.2140.2140.2580.0050.0000.1990.2170.2080.0340.0300.0030.180
member_card0.0000.5240.3440.3440.0430.0340.3570.0380.0000.0000.0000.0480.0390.0550.0010.1200.0000.2360.0480.0321.0000.0000.3540.2480.0030.0550.0000.0030.0370.0680.0080.0120.0530.0560.0270.1440.0360.0000.020
net_weight0.0430.0000.0000.0000.0090.0020.0060.0000.1640.1170.085-0.0040.000-0.0030.9890.0000.0770.000-0.0040.0000.0001.000-0.0020.0000.0040.0000.0900.0040.0000.0000.0350.036-0.0050.0000.0050.0000.001-0.0120.000
num_children_at_home-0.0010.0490.1010.1010.030-0.0030.0520.0260.0000.0030.007-0.0150.0500.025-0.0020.2380.0000.783-0.0150.0340.354-0.0021.0000.0440.0520.0670.0000.0520.0530.0820.0140.0170.0090.0600.0350.3180.044-0.0020.046
occupation0.0000.4330.1620.1620.0290.0340.4360.0280.0000.0000.0000.0550.0310.0590.0000.1040.0000.0340.0550.0340.2480.0000.0441.0000.0240.0680.0000.0240.0230.0810.0070.0030.0560.0550.0200.0350.0000.0000.009
prepared_food0.0000.0280.0430.0430.5310.2010.0130.6420.0000.0060.0030.6640.0270.6600.0020.0110.0000.0210.6640.2140.0030.0040.0520.0241.0000.4050.0001.0000.2900.9750.0410.0380.6780.5010.8250.0330.1470.0080.620
promotion_name0.0070.0660.0570.0570.4670.4710.0600.4490.0000.0000.0000.4120.0550.4230.0000.0590.0000.0720.4120.4620.0550.0000.0670.0680.4051.0000.0120.4050.4570.4040.0120.0130.4230.4240.3730.0650.0600.0000.486
recyclable_package0.0930.0070.0000.0000.0000.0000.0120.0000.1990.1160.0380.0090.0000.0000.0900.0000.0260.0000.0090.0000.0000.0900.0000.0000.0000.0121.0000.0000.0000.0000.0330.0430.0000.0000.0000.0000.0000.0560.000
salad_bar0.0000.0280.0430.0430.5310.2010.0130.6420.0000.0060.0030.6640.0270.6600.0020.0110.0000.0210.6640.2140.0030.0040.0520.0241.0000.4050.0001.0000.2900.9750.0410.0380.6780.5010.8250.0330.1470.0080.620
sales_country0.0000.0400.0580.0580.2210.1980.0180.2560.0000.0000.0040.6840.0600.6940.0060.0140.0130.0190.6840.2140.0370.0000.0530.0230.2900.4570.0000.2901.0001.0000.0050.0110.5531.0000.3460.0370.0300.0000.350
store_city0.0000.0760.0870.0870.9750.2430.0650.9760.0000.0000.0000.9940.0980.9510.0000.0730.0150.0810.9940.2580.0680.0000.0820.0810.9750.4040.0000.9751.0001.0000.0620.0620.9881.0000.9640.0700.3050.0050.974
store_cost(in millions)0.8270.0030.0000.0000.028-0.0070.0060.0370.0630.0400.0410.0240.0000.0120.0390.0000.0060.0000.0240.0050.0080.0350.0140.0070.0410.0120.0330.0410.0050.0621.0000.9660.0160.0330.0930.0650.454-0.0140.029
store_sales(in millions)0.8550.0000.0020.0020.029-0.0070.0090.0390.0780.0540.0540.0240.0000.0100.0400.0050.0150.0070.0240.0000.0120.0360.0170.0030.0380.0130.0430.0380.0110.0620.9661.0000.0150.0330.0940.0690.471-0.0140.027
store_sqft0.0010.0560.0550.0550.578-0.0430.0480.8000.0000.0000.0000.7500.0590.900-0.0050.0490.0100.0580.7500.1990.053-0.0050.0090.0560.6780.4230.0000.6780.5530.9880.0160.0151.0000.7230.7190.0020.0230.0020.706
store_state0.0020.0540.0630.0630.5310.1920.0520.6720.0000.0000.0000.7410.0960.6750.0000.0540.0140.0490.7410.2170.0560.0000.0600.0550.5010.4240.0000.5011.0001.0000.0330.0330.7231.0000.6150.0510.1500.0060.696
store_type0.0050.0440.0390.0390.8270.1490.0270.8620.0000.0000.0080.6120.0280.6060.0020.0170.0060.0340.6120.2080.0270.0050.0350.0200.8250.3730.0000.8250.3460.9640.0930.0940.7190.6151.0000.0260.3410.0000.762
total_children-0.0000.0530.0700.0700.021-0.0040.0540.0130.0000.0000.000-0.0220.0590.0170.0000.0570.0000.061-0.0220.0340.1440.0000.3180.0350.0330.0650.0000.0330.0370.0700.0650.0690.0020.0510.0261.0000.1540.0030.030
unit_sales(in millions)-0.0030.0180.0150.0150.101-0.0140.0170.1400.0060.0070.0000.0310.0000.0190.0010.0090.0000.0150.0310.0300.0360.0010.0440.0000.1470.0600.0000.1470.0300.3050.4540.4710.0230.1500.3410.1541.000-0.0000.091
units_per_case-0.0150.0000.0000.0000.007-0.0030.0000.0000.1800.1280.083-0.0000.0000.003-0.0090.0000.0800.000-0.0000.0030.000-0.012-0.0020.0000.0080.0000.0560.0080.0000.005-0.014-0.0140.0020.0060.0000.003-0.0001.0000.000
video_store0.0000.0510.0450.0450.5890.1930.0290.6530.0090.0050.0100.6540.0110.7320.0090.0110.0020.0070.6540.1800.0200.0000.0460.0090.6200.4860.0000.6200.3500.9740.0290.0270.7060.6960.7620.0300.0910.0001.000

Missing values

2024-09-15T13:14:30.162023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T13:14:31.128090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

food_categoryfood_departmentfood_familystore_sales(in millions)store_cost(in millions)unit_sales(in millions)promotion_namesales_countrymarital_statusgendertotal_childreneducationmember_cardoccupationhouseowneravg_cars_at home(approx)avg. yearly_incomenum_children_at_homeavg_cars_at home(approx).1brand_nameSRPgross_weightnet_weightrecyclable_packagelow_fatunits_per_casestore_typestore_citystore_statestore_sqftgrocery_sqftfrozen_sqftmeat_sqftcoffee_barvideo_storesalad_barprepared_foodfloristmedia_typecost
0Breakfast FoodsFrozen FoodsFood7.362.72324Bag StuffersUSAMF1Partial High SchoolNormalSkilled ManualY1$10K - $30K11Carrington1.8419.7017.701017Deluxe SupermarketSalemOR27694186705415361011111Daily Paper, Radio126.62
1Breakfast FoodsFrozen FoodsFood5.522.59443Cash Register LotteryUSAMM0Bachelors DegreeSilverProfessionalY4$50K - $70K04Carrington1.8419.7017.701017Deluxe SupermarketSalemOR27694186705415361011111Daily Paper, Radio59.86
2Breakfast FoodsFrozen FoodsFood3.681.36162High Roller SavingsUSASF4Partial High SchoolNormalManualN1$10K - $30K01Carrington1.8419.7017.701017Deluxe SupermarketSalemOR27694186705415361011111Daily Paper, Radio84.16
3Breakfast FoodsFrozen FoodsFood3.681.17762Cash Register LotteryUSAMF2High School DegreeBronzeManualY2$30K - $50K22Carrington1.8419.7017.701017Deluxe SupermarketSalemOR27694186705415361011111In-Store Coupon95.78
4Breakfast FoodsFrozen FoodsFood4.081.42803Double Down SaleUSAMM0Partial High SchoolBronzeSkilled ManualN2$30K - $50K02Golden1.367.125.110129Deluxe SupermarketSalemOR27694186705415361011111Radio50.79
5Breakfast FoodsFrozen FoodsFood4.081.46883Double Down SaleUSAMF2Bachelors DegreeBronzeProfessionalN1$50K - $70K21Golden1.367.125.110129Deluxe SupermarketSalemOR27694186705415361011111Radio50.79
6Breakfast FoodsFrozen FoodsFood5.442.55684Cash Register LotteryUSASF4High School DegreeBronzeSkilled ManualN2$30K - $50K02Golden1.367.125.110129Deluxe SupermarketSalemOR27694186705415361011111In-Store Coupon95.78
7Breakfast FoodsFrozen FoodsFood3.741.60822Cash Register LotteryUSASM1Partial High SchoolBronzeManualY4$50K - $70K04Imagine1.8716.7014.701110Deluxe SupermarketSalemOR27694186705415361011111Daily Paper, Radio59.86
8Breakfast FoodsFrozen FoodsFood4.081.46883Cash Register LotteryUSASF2Partial High SchoolNormalSkilled ManualN2$10K - $30K02Golden1.367.125.110129Deluxe SupermarketSalemOR27694186705415361011111Daily Paper, Radio59.86
9Breakfast FoodsFrozen FoodsFood9.724.56843High Roller SavingsUSASF3Graduate DegreeBronzeProfessionalN1$70K - $90K01Big Time3.2416.3014.201025Deluxe SupermarketSalemOR27694186705415361011111Daily Paper, Radio84.16
food_categoryfood_departmentfood_familystore_sales(in millions)store_cost(in millions)unit_sales(in millions)promotion_namesales_countrymarital_statusgendertotal_childreneducationmember_cardoccupationhouseowneravg_cars_at home(approx)avg. yearly_incomenum_children_at_homeavg_cars_at home(approx).1brand_nameSRPgross_weightnet_weightrecyclable_packagelow_fatunits_per_casestore_typestore_citystore_statestore_sqftgrocery_sqftfrozen_sqftmeat_sqftcoffee_barvideo_storesalad_barprepared_foodfloristmedia_typecost
60418SpecialtyCarouselNon-Consumable8.282.73243Save-It SaleMexicoMM2Partial CollegeGoldenManagementN4$30K - $50K14ADJ2.7619.618.61026SupermarketAcapulcoGuerrero23593174753671244700000In-Store Coupon67.63
60419SpecialtyCarouselNon-Consumable6.902.82903Two Day SaleMexicoSM4Bachelors DegreeGoldenProfessionalN2$70K - $90K02Prelude2.3021.519.51029SupermarketAcapulcoGuerrero23593174753671244700000Radio73.27
60420SpecialtyCarouselNon-Consumable4.841.69404Price WinnersMexicoSF1Partial High SchoolNormalSkilled ManualY1$10K - $30K01Toretti1.2118.915.80026SupermarketAcapulcoGuerrero23593174753671244700000Sunday Paper112.19
60421SpecialtyCarouselNon-Consumable0.990.45541Green Light SpecialUSASF2High School DegreeBronzeProfessionalY3$130K - $150K03King0.9911.710.61025Small GrocerySan FranciscoCA22478153214294286310000Cash Register Handout127.19
60422SpecialtyCarouselNon-Consumable1.210.44771Unbeatable Price SaversUSASF1Partial High SchoolBronzeSkilled ManualN2$50K - $70K02Toretti1.2118.915.80026Small GrocerySan FranciscoCA22478153214294286310000Sunday Paper, Radio78.45
60423SpecialtyCarouselNon-Consumable2.761.32481You Save DaysUSAMF1Partial High SchoolNormalSkilled ManualY1$10K - $30K11ADJ2.7619.618.61026Small GrocerySan FranciscoCA22478153214294286310000In-Store Coupon95.25
60424SpecialtyCarouselNon-Consumable1.600.49601Price CuttersUSASF2High School DegreeBronzeSkilled ManualN2$30K - $50K02Symphony1.6017.415.31036Small GrocerySan FranciscoCA22478153214294286310000Sunday Paper69.42
60425SpecialtyCarouselNon-Consumable5.522.53922Weekend MarkdownUSAMM1High School DegreeBronzeManualY3$30K - $50K03ADJ2.7619.618.61026Small GrocerySan FranciscoCA22478153214294286310000Sunday Paper, Radio, TV67.51
60426SpecialtyCarouselNon-Consumable8.282.56683Sales DaysCanadaSM2Bachelors DegreeBronzeProfessionalN4$70K - $90K04ADJ2.7619.618.61026Mid-Size GroceryVictoriaBC34452274634193279510001Sunday Paper132.88
60427SpecialtyCarouselNon-Consumable9.204.23204Super Duper SaversCanadaSF3Partial High SchoolBronzeManualY1$10K - $30K01Prelude2.3021.519.51029Mid-Size GroceryVictoriaBC34452274634193279510001Daily Paper, Radio87.76